[Artificial intelligence for pathology-how, where, and why?] / Künstliche Intelligenz in der Pathologie ­ wie, wo und warum?

Artificial intelligence promises many innovations and simplifications in pathology, but also raises just as many questions and uncertainties. In this article, we provide a brief overview of the current status, the goals already achieved by existing algorithms, and the remaining challenges.

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.

Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls.

The use of digitized data in pathology research is rapidly increasing. The whole slide image (WSI) is an indispensable part of the visual examination of slides in digital pathology and artificial intelligence applications; therefore, the acquisition of WSI with the highest quality is essential. Unlike the conventional routine of pathology, the digital conversion of tissue slides and the differences in its use pose difficulties for pathologists. We categorized these challenges into three groups: before, during, and after the WSI acquisition. The problems before WSI acquisition are usually related to the quality of the glass slide and reflect all existing problems in the analytical process in pathology laboratories. WSI acquisition problems are dependent on the device used to produce the final image file. They may be related to the parts of the device that create an optical image or the hardware and software that enable digitization. Post-WSI acquisition issues are related to the final image file itself, which is the final form of this data, or the software and hardware that will use this file. Because of the digital nature of the data, most of the difficulties are related to the capabilities of the hardware or software. Being aware of the challenges and pitfalls of using digital pathology and AI will make pathologists' integration to the new technologies easier in their daily practice or research.

Transformación de una Unidad de Cuidados Paliativos Pediátricos en Unidad de Atención Domiciliaria a raíz de la pandemia COVID-19. ¿Llegó para quedarse? / The Paediatric Palliative Care Unit has been transformed into Home Care Unit during the COVID-19 pandemic. Is this transformation for the foreseeable future?

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Digital pathology and artificial intelligence in translational medicine and clinical practice.

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Trends in cytopathology fellowship positions and vacancies over the past decade.

INTRODUCTION: Cytopathology is one of the most sought-after fellowships within pathology, with a lower fellowship vacancy rate compared with most other subspecialties. The Accreditation Council for Graduate Medical Education (ACGME) actively tracks annual program data for cytopathology fellowship programs, and evaluating this longitudinal data looking at trends in programs and positions over the past 10 years could provide insights into the future of cytopathology and its training programs. METHODS: Data obtained from the ACGME was examined in detail for all ACGME-accredited cytopathology fellowship programs over the past decade (2011-2021). Additional responses from program directors (PDs) from a 2021 American Society of Cytopathology (ASC) survey are also included. RESULTS: The total number of ACGME-approved cytopathology training programs and cytopathology fellowship positions remained relatively constant over the past 10 years, but the vacancy rate and number of programs with 1-2 unfilled spots has gradually but steadily risen over the past 6 years. In a 2021 ASC PD survey with 66% response rate, 53% of PDs reported having recruitment problems at least occasionally and 46% reported an increase in unexpected fellowship openings. CONCLUSIONS: Although the number of cytopathology positions has been relatively constant over the past decade, there has been a recent increase in cytopathology fellowship vacancies that may indicate changes in career choices or the job market, with fellows choosing jobs over additional fellowships, and potentially signal a growing shortage of fellowship-trained, Board-certified cytopathologists in the coming years.

Flattening the World of Pathology Education and Training and Shortening the Curve of Pathology Learning.

OBJECTIVES: We review how the pandemic-related education disruption may interplay with pathology manpower worldwide and shifts in disease burden to identify workable solutions. METHODS: Literature related to pathology education, pathology services in low-resource settings, and application of digital tools to pathology education was reviewed for trends and training gaps. Publications covering pathology manpower and cancer incidence worldwide were also included to assess needs. RESULTS: Pandemic-related virtual teaching has produced abundant online training materials. Pathology learning resources in low- to middle-income countries remain considerably constrained and dampen pathology manpower growth to meet current needs. Projected increases in disease burden toward the developing world thus pose a major challenge. Digital pathology resources have expanded and are beginning to appear beyond the developed countries. CONCLUSIONS: This circumstance offers a unique opportunity to leverage digital teaching resources to enhance and equitize training internationally, potentially sufficient to meet the rising wave of noncommunicable diseases. We propose four next steps to take advantage of the current opportunity: curate and organize digital training materials, invest in the digital pathology infrastructure for education and clinical care, expand student exposure to pathology through virtual electives, and develop further competency-based certification pathways.

Next-Generation Digital Histopathology of the Tumor Microenvironment.

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.

Can low autopsy rates be increased? Yes, we can! Should postmortem examinations in oncology be performed? Yes, we should! A postmortem analysis of oncological cases.

Ever declining autopsy rates have been a concern of pathologists as well as clinicians for decades. Notably, in the field of oncology, data on autopsies and discrepancies between clinical and autoptic diagnoses are particularly scarce. In this retrospective study, we show the effect of a simple catalog of measures consisting of a different approach to obtain consent for autopsy, structured conferencing, and systematic teaching of residents, as well as a close collaboration between clinicians and pathologists on the numbers of autopsies, especially of oncological patients. Additionally, postmortem examination protocols from the years 2015 until 2019 were analyzed, regarding rates of discrepancies between clinical and autoptic causes of death in this category of patients. Autopsy numbers could be significantly increased from a minimum in 2014 (60 autopsies) to a maximum in 2018 (142 autopsies) (p < 0.0001). In the 67 autopsies of oncological cases, a high rate of 51% of major discrepancy between clinical and autoptic causes of death could be detected. In contrast to the general reported decline of autopsy rates, we present rising autopsy numbers over the past 5 years with an increasing number of oncological cases who underwent a postmortem examination. The high percentage of major discrepancies between clinical and autopsy diagnosis is in contrast to an expected decrease of major discrepancies in times of precise diagnostic methods and underlines the importance of autopsies to ensure high quality in diagnostics and therapy not only in the field of oncology.